In this letter, we propose a residual network (ResNet) based AMC scheme using involution [1]}. The contributions are summarized as follows. Firstly, involution is utilized instead of convolution to enhance the discrimination capability and expressiveness of the model by incorporating a self-attention mechanism. Secondly, a novel network that inherits the advantages of residual learning and involution is designed to learn high-dimensional representations of different modulations from I/Q signals and construct the classifier. Thirdly, simulation results demonstrate the effectiveness of the proposed involution based ResNet for AMC. It is shown that our proposed scheme has a better classification performance and a faster convergence speed.
